Automatic Load Sharing of Transformer: A Comprehensive Review

Abstract

The demand for efficient and reliable electrical power distribution systems has grown significantly with the increasing complexity of modern electrical grids. Transformers play a crucial role in these systems, serving as key components in the transmission and distribution of electrical energy. To enhance the performance and reliability of transformer hardware, automatic load sharing mechanisms have become essential. This review paper aims to provide an in-depth analysis of the various automatic load sharing methods employed in transformer hardware to optimize energy distribution, improve system reliability, and ensure efficient load management.

Introduction

I. INTRODUCTION

Transformers is one of the most significant equipment in the electrical power system, hence transformer required protection. Apart from this the demand for electricity is increasing due to the increasing population and their unavoidable demands, with this increased power requirement, the existing systems have become overloaded. The overloading appears at the consumer end of the transformer terminals, which can affect its efficiency and protection systems. Due to overload on the transformer the efficiency drops and the windings gets over heated and may get burnt. It takes a lot of time to repair and lot of expenditure. Transformers are occasionally loaded beyond nameplate ratings because of existing possible contingencies on the transmission lines, any failure or fault in power systems, or economic considerations. One of the reported damage or tripping of the distribution transformer is due to thermal overload. To eliminate the damaging of transformers due to overloading from consumer end, it involves the control against over current tripping of distribution transformer. Rise in operating temperature of the transformer. The project is all about protecting the transformer under overload condition. by connecting another transformer in parallel through a microcontroller and a relay which shares the excess load of the first transformer. The transformers are switched alternatively to avoid thermal overloading. Therefore, two transformers work efficiently under overload condition and damage can be prevented. If there is a further increase in load beyond the capacity of two transformers there will be a priority-based load shedding of consumers which will provide un-interrupted power supply for the hospitals, industries etc. Transformers play a pivotal role in electrical distribution systems, facilitating power transmission over long distances and stepping down voltage for safe consumption. As the demand for electrical energy grows in today's technological landscape, transformers become even more essential, grappling with challenges like varying loads and disruptions. The uneven distribution of loads on transformers within modern grids poses a significant challenge. Manual load-sharing methods are inadequate for the complexities of today's grids, leading to inefficiency and equipment risks. Automatic load sharing of transformer hardware emerges as a solution, utilizing advanced technologies like microcontrollers and adaptive algorithms to optimize energy distribution, enhance reliability, and extend infrastructure lifespan. This comprehensive review delves into automatic load sharing of transformer hardware, exploring transformer types, load-sharing principles, historical limitations, and recent advances. The paper addresses challenges, cybersecurity considerations, and real-world case studies, aiming to be a valuable resource for researchers and decision-makers in the field. The significance of transformers in electrical distribution is undeniable, and the need for automatic load sharing is more critical than ever. This review provides insights into the past, present, and future of this technology, offering guidance for ensuring the reliability and efficiency of electrical power distribution systems.

II. LITERATURE SURVEY

Develop an efficient and accurate approach for NILM, which involves inferring appliance consumption patterns solely from aggregated household signals. The project introduces Electricity, a transformer-based architecture for NILM, offering superior performance, efficiency, and reduced training time compared to existing methods. The outcomes suggest that Electricity is a promising approach for accurate and efficient Non-Intrusive Load Monitoring in household energy consumption scenarios. Discuss about methodology of Utilizes sequence-to-sequence deep learning models based on transformer layers. Transformer architecture relies on attention mechanisms to extract global dependencies between aggregated and appliance signals. model Electricity to reflect its focus on electricity consumption patterns. [1]

A. Types Of Transformers

1. Distribution Transformers

Distribution transformers are commonly found at the tail-end of the electrical grid, where they reduce the voltage for safe use in homes and commercial buildings. These transformers typically have lower power ratings and are often located near consumers, ensuring a consistent and reliable power supply. Commonly used in residential, commercial, and industrial areas. Multiple distribution transformers can be connected in parallel to share the load. Adjust the turns ratio to maintain a constant output voltage.

2. Power Transformers

Power transformers are situated at the heart of the electrical grid, serving as crucial elements in high-voltage transmission networks. These transformers handle substantial power capacities and are responsible for stepping up or stepping down voltage levels to facilitate long-distance power transmission. Higher voltage ratings compared to distribution transformers. Connect different parts of the power grid, such as generators, transmission lines, and distribution networks. Used for transmitting electrical power over long distances. Connect multiple power transformers in parallel to share the load. Adjust the tap settings to control the output voltage. adjust taps even during operation to accommodate load changes.

3. Special Purpose Transformers

Special purpose transformers serve specific functions in the electrical system. They can include instrument transformers, rectifier transformers, furnace transformers, and others. These transformers cater to niche requirements, and their load-sharing techniques vary based on the particular application. For instance, rectifier transformers used in industrial settings may utilize complex control algorithms to manage load sharing in response to changing manufacturing processes. Some special purpose transformers are designed to synchronize their operation with process variables, ensuring that the load-sharing mechanism aligns with specific industrial requirements.

B. Comparison Table

In this table, we explore various approaches and technologies applied to the domain of electrical systems and load management. The following table presents a comparative analysis of different studies.

Conclusion

In today\'s advanced power systems, having a dependable and effective way to distribute electrical energy is more important than ever. As our electrical grids become more complex, it\'s crucial to make sure that energy is distributed efficiently and reliably. Automatic load sharing is really important because it helps us manage energy effectively. By sharing the load among different transformers, we can make sure that the energy is distributed optimally, making the system more reliable and efficient. Researchers are exploring new and smart ways to improve our electrical systems. Integrating IoT technology, which connects devices and systems over the internet, is making our power transformers safer and more efficient. This helps in detecting faults early and ensures that our power systems work smoothly.

References

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Copyright

Copyright © 2024 Vandana Chatse , G. D. Shingade. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.